Several systems have recently been developed for determining whether an individual (or a biometric sample derived from the individual), is present in a small database of biometric information (of, say, fingerprints, or iris scans). Because these biometric measurements may be somewhat imprecise, identification of the set of individuals who match two hundred out of a possible set of three hundred characteristics of a sampled individual in a database of several billion individuals is currently beyond what existing systems can do.
Existing methods require comparing the entire collection of characteristics for a sampled individual against the entire database of characteristics for known individuals. Exemplary characteristics in the case where the biometric data represents an eye scan includes various characteristics of the iris, including eye color, the number of radial furrows and the number of concentric furrows in the iris, the size, number, shape and/or location of moles, freckles and crypts within the iris, and so forth. Known techniques require comparisons proportional to the number of characteristics in each sample multiplied by the number of individuals in the database. However, systems that can practically scale to handle a database of individuals of the size of the number of ATM users in the world, or the number of passport holders, or the total number of people are not found in the known art.
As the underlying technologies for biometric identification have improved (cf. www. sensar.com, www.identicator.com, and U.S. Pat. No. 4,641,349 for examples), the systems for handling the problem of matching an unknown individual or biometric sample against a small set of people or known biometric samples has improved significantly, to the point where such systems can reasonably be used to allow an individual to log on to her computer. The techniques tend to involve the comparison of hundreds of extracted characteristics for near-equality.
A fundamental problem with known techniques is that they do not provide a robust solution for comparing the variable characteristics found in biometric data. Variable characteristics are broadly defined as those characteristics that are represented by one value, but in fact, the true value for the characteristic is any of a range of values about the given value. Variable characteristics arise in biometric data due to inaccuracies in measuring the underlying biometric sample, qualitative assumptions made when examining the biometric sample, etc.
Given the above background, what is needed in the art are systems and methods for effectively comparing a query biometric sample against a large set of target biometric samples. Such systems and methods must provide robust solutions to the problem of comparing variable characteristics typically associated with biometric data.